Meta-heuristic algorithms have shown great capacity and adaptability in finding global minima in different test problems and practical applications over the years. However, these types of algorithms are always in constant improvement since according to the No Free Lunch (NFL) theorem, no meta-heuristic can optimize all kinds of problems [1]}. As a result of the need to optimize increasingly complex problems with a larger number of dimensions, the emergence of new meta-heuristic algorithms has increased in recent years [2]}.
